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In the current era of big data and machine learning, a strong focus exists on prediction and classification. In industrial applications, however, many important questions are not about prediction or classification; rather, they are causal: if I change A, what will happen to B? Traditional regression techniques such as machine learning optimize predictions based on correlations seen in the data and are not robust tools for epidemiologists and biostatisticians when evaluating the efficacy of new treatments or medications using observational data. Therefore, a set of statistical tools have been developed to go beyond correlations and aim to make inferences about causal relationships between variables. The goal of the present work is to apply one of these statistical tools, propensity score matching, in the oil and gas context, which is a novel application of the method. Two case studies are presented, one on proppant type and the other on lateral length, to determine their respective impacts on productivity.

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